Embedded Vector Index Store

Data storeKnowledge & DataKnowledge & Dataarc:EmbeddedVectorIndexStore

A lightweight vector store that runs in-process inside the application as a library, requiring no separate services, network API or infrastructure.

Responsibility. Stores and searches vectors in-process for local development and small collections.

Also known as: In-process vector database, Lightweight development vector store

Variant of Vector Index Store abstract

When to choose. Choose for local development, notebooks, proofs of concept, education and embedded applications with modest scale (well under one million vectors).

Direct neighbourhood (hover for relationship types)

Relationships

is read by dependency

Vector Retriever abstract Ref7.07

is written by dependency

Text Embedding Service Ref7.07

alternative to variability

Design guidance

SHOULD NOT be used where horizontal scaling, advanced index types, high availability or production monitoring are required.

Quantitative guidance

As stated by the sources; verify before use.

Targets <1 million vectors; performance degrades substantially beyond that (Ch6.2A).
Suited to single-node deployments up to ~1M documents (Ref7.07).

Classification

Patterns: Prototype locally, migrate to production-grade store
Technologies: ChromaFAISS
Quality attributes: Maintainability (ISO/IEC 25010)

Sources

Ch6.2A: T. Nguyen, "Vector Database Selection," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.2A. ISBN: 9798244538229.
Ref7.07: E. Li, V. Bellotti, R. Kraus, and R. Kao, "Build a retrieval-augmented generation (RAG) agent with NVIDIA Nemotron," NVIDIA Technical Blog, Sep. 23, 2025. [Online]. Available: https://developer.nvidia.com/blog/build-a-rag-agent-with-nvidia-nemotron/